Extreme giant molecular clouds in the luminous infrared galaxy NGC 3256
Bibliographic record
Abstract
ABSTRACT We present a cloud decomposition of 12CO (2–1) observations of the merger and nearest luminous infrared galaxy, NGC 3256. 185 spatially and spectrally resolved clouds are identified across the central ≈130 kpc2 at 90 pc resolution and completeness is estimated. We compare our cloud catalogue from NGC 3256 to ten galaxies observed in the PHANGS-ALMA survey. Distributions in NGC 3256 of cloud velocity dispersions (median 23 km s−1), luminosities (1.5 × 107 K km s−1 pc2), CO-estimated masses (2.1 × 107 M⊙), mass surface densities (470 M⊙ pc−2), virial masses (5.4 × 107 M⊙), virial parameters (4.3), size-linewidth coefficients (6.3 km2 s−2 pc−1), and internal turbulent pressures ( 1.0 × 10 7K cm−3$\, k_{\mathrm{B}}^{-1}$) are significantly higher than in the PHANGS-ALMA galaxies. Cloud radii (median 88 pc) are slightly larger in NGC 3256 and free-fall times (4.1 Myr) are shorter. The distribution of cloud eccentricities in NGC 3256 (median of 0.8) is indistinguishable from many PHANGS-ALMA galaxies, possibly because the dynamical state of clouds in NGC 3256 is similar to that of nearby spiral galaxies. However, the narrower distribution of virial parameters in NGC 3256 may reflect a narrower range of dynamical states than in PHANGS-ALMA galaxies. No clear picture of cloud alignment is detected, despite the large eccentricities. Correlations between cloud properties point to high external pressures in NGC 3256 keeping clouds bound and collapsing given such high velocity dispersions and star-formation rates. A fit to the cloud mass function gives a high-mass power-law slope of $-2.75^{+0.07}_{-0.01}$, near the average from PHANGS-ALMA galaxies. We also compare our results to a pixel-based analysis of these observations and find molecular-gas properties agree qualitatively, though peak brightness temperatures are somewhat higher and virial parameters and free-fall times are somewhat lower in this cloud-based analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".